Using High-Resolution Bus Detection Data to Improve Travel Time Prediction and Identify Urban Congestion Spots

Miguel Andres Figliozzi, Portland State University


  • Robert Bertini, Portland State University


Most urban vehicle miles traveled take place along arterials and collector streets.  Arterials and collector streets are also essential to provide accessibility and provide mobility for public transportation fleets; these roadways constitute the backbone of bus routes in urban areas. However, unlike freeways, arterials and collectors are still relatively understudied and do not have a large body of work related to congestion quantification and performance measures (PMs). This proposal is timely because: transit agencies necessitate to quantify congestion and performance measures along urban arterials and collector streets to adjust and improve bus operations; policy requirements are demanding more highly granular PMs for local, regional and statewide agencies; and researchers and practitioners have relatively modest traffic data along arterials (along arterials traffic conditions and vehicle flows change significantly at or between major intersections; on the other hand, freeways have limited access and often metered on/off ramps). 

This research proposes the use of new datasets: vehicle wheel motion (VWM) detection data and TriMet’s 5-second interval, automatic vehicle location (AVL) data.  In partnership with TriMet (the public transit agency in the Portland metropolitan region) and ODOT/Transport (the state DOT and the body that coordinates the work of all the transportation related agencies in the Portland metropolitan area), the research team will utilize these new datasets to better quantify congestion and transportation PMs in the Portland region. More specifically this research will utilize these novel datasets and compare/integrate them with other available datasets to: (1) develop a new methodology to exploit new data sources to identify/quantify congested segments along arterials and collector streets; (2) provide a method that TriMet can use to improve its operations and update its routes/schedules; and (3) explore ancillary applications of the data such as the estimation of performance measures and the identification of air quality hotspots.

Project Details

Project Type:
Project Status:
End Date:
June 30,2016
UTC Grant Cycle:
Tier 1 Round 3
UTC Funding:

Other Products

  • Glick, T. B., & Figliozzi, M. A. (2018). Evaluation of Route Changes Utilizing High-Resolution GPS Bus Transit Data. Transportation Research Record, 2672(8), 199-209. (PUBLICATION)
  • Figliozzi, M. A., & Stoll, N. B. (2018). A Study of Bus High-Resolution GPS Speed Data Accuracy. Transportation Research Record, 2672(8), 187-198. (PUBLICATION)
  • Using High-Resolution Bus GPS Data to Visualize and Identify Congestion Hot Spots in Urban Arterials (PRESENTATION)
  • Exploring Applications of Second Generation Archived Transit Data for Estimating Performance Measures and Arterial Travel Speeds (PRESENTATION)